// Finds vertical lines in the given list of BLOBNBOXes. bleft and tright
// are the bounds of the image on which the input line_bblobs were found.
// The input line_bblobs list is const really.
// The output vertical_x and vertical_y are the total of all the vectors.
// The output list of TabVector makes no reference to the input BLOBNBOXes.
void LineFinder::FindLineVectors(const ICOORD& bleft, const ICOORD& tright,
BLOBNBOX_LIST* line_bblobs,
int* vertical_x, int* vertical_y,
TabVector_LIST* vectors) {
BLOBNBOX_IT bbox_it(line_bblobs);
int b_count = 0;
// Put all the blobs into the grid to find the lines, and move the blobs
// to the output lists.
AlignedBlob blob_grid(kLineFindGridSize, bleft, tright);
for (bbox_it.mark_cycle_pt(); !bbox_it.cycled_list(); bbox_it.forward()) {
BLOBNBOX* bblob = bbox_it.data();
bblob->set_left_tab_type(TT_UNCONFIRMED);
bblob->set_left_rule(bleft.x());
bblob->set_right_rule(tright.x());
bblob->set_left_crossing_rule(bleft.x());
bblob->set_right_crossing_rule(tright.x());
blob_grid.InsertBBox(false, true, bblob);
++b_count;
}
if (textord_debug_tabfind)
tprintf("Inserted %d line blobs into grid\n", b_count);
if (b_count == 0)
return;
// Search the entire grid, looking for vertical line vectors.
GridSearch<BLOBNBOX, BLOBNBOX_CLIST, BLOBNBOX_C_IT> lsearch(&blob_grid);
BLOBNBOX* bbox;
TabVector_IT vector_it(vectors);
*vertical_x = 0;
*vertical_y = 1;
lsearch.StartFullSearch();
while ((bbox = lsearch.NextFullSearch()) != NULL) {
if (bbox->left_tab_type() == TT_UNCONFIRMED) {
const TBOX& box = bbox->bounding_box();
if (AlignedBlob::WithinTestRegion(2, box.left(), box.bottom()))
tprintf("Finding line vector starting at bbox (%d,%d)\n",
box.left(), box.bottom());
AlignedBlobParams align_params(*vertical_x, *vertical_y, box.width());
TabVector* vector = blob_grid.FindVerticalAlignment(align_params, bbox,
vertical_x,
vertical_y);
if (vector != NULL) {
vector->Freeze();
vector_it.add_to_end(vector);
}
}
}
ScrollView* line_win = NULL;
if (textord_tabfind_show_vlines) {
line_win = blob_grid.MakeWindow(0, 50, "Vlines");
blob_grid.DisplayBoxes(line_win);
line_win = blob_grid.DisplayTabs("Vlines", line_win);
}
}
// Tests each blob in the list to see if it is certain non-text using 2
// conditions:
// 1. blob overlaps a cell with high value in noise_density_ (previously set
// by ComputeNoiseDensity).
// OR 2. The blob overlaps more than max_blob_overlaps in *this grid. This
// condition is disabled with max_blob_overlaps == -1.
// If it does, the blob is declared non-text, and is used to mark up the
// nontext_mask. Such blobs are fully deleted, and non-noise blobs have their
// neighbours reset, as they may now point to deleted data.
// WARNING: The blobs list blobs may be in the *this grid, but they are
// not removed. If any deleted blobs might be in *this, then this must be
// Clear()ed immediately after MarkAndDeleteNonTextBlobs is called.
// If the win is not NULL, deleted blobs are drawn on it in red, and kept
// blobs are drawn on it in ok_color.
void CCNonTextDetect::MarkAndDeleteNonTextBlobs(BLOBNBOX_LIST* blobs,
int max_blob_overlaps,
ScrollView* win,
ScrollView::Color ok_color,
Pix* nontext_mask) {
int imageheight = tright().y() - bleft().x();
BLOBNBOX_IT blob_it(blobs);
BLOBNBOX_LIST dead_blobs;
BLOBNBOX_IT dead_it(&dead_blobs);
for (blob_it.mark_cycle_pt(); !blob_it.cycled_list(); blob_it.forward()) {
BLOBNBOX* blob = blob_it.data();
TBOX box = blob->bounding_box();
if (!noise_density_->RectMostlyOverThreshold(box, max_noise_count_) &&
(max_blob_overlaps < 0 ||
!BlobOverlapsTooMuch(blob, max_blob_overlaps))) {
blob->ClearNeighbours();
#ifndef GRAPHICS_DISABLED
if (win != NULL)
blob->plot(win, ok_color, ok_color);
#endif // GRAPHICS_DISABLED
} else {
if (noise_density_->AnyZeroInRect(box)) {
// There is a danger that the bounding box may overlap real text, so
// we need to render the outline.
Pix* blob_pix = blob->cblob()->render_outline();
pixRasterop(nontext_mask, box.left(), imageheight - box.top(),
box.width(), box.height(), PIX_SRC | PIX_DST,
blob_pix, 0, 0);
pixDestroy(&blob_pix);
} else {
if (box.area() < gridsize() * gridsize()) {
// It is a really bad idea to make lots of small components in the
// photo mask, so try to join it to a bigger area by expanding the
// box in a way that does not touch any zero noise density cell.
box = AttemptBoxExpansion(box, *noise_density_, gridsize());
}
// All overlapped cells are non-zero, so just mark the rectangle.
pixRasterop(nontext_mask, box.left(), imageheight - box.top(),
box.width(), box.height(), PIX_SET, NULL, 0, 0);
}
#ifndef GRAPHICS_DISABLED
if (win != NULL)
blob->plot(win, ScrollView::RED, ScrollView::RED);
#endif // GRAPHICS_DISABLED
// It is safe to delete the cblob now, as it isn't used by the grid
// or BlobOverlapsTooMuch, and the BLOBNBOXes will go away with the
// dead_blobs list.
// TODO(rays) delete the delete when the BLOBNBOX destructor deletes
// the cblob.
delete blob->cblob();
dead_it.add_to_end(blob_it.extract());
}
}
}
// Extend this vector to include the supplied blob if it doesn't
// already have it.
void TabVector::ExtendToBox(BLOBNBOX* new_blob) {
TBOX new_box = new_blob->bounding_box();
BLOBNBOX_C_IT it(&boxes_);
if (!it.empty()) {
BLOBNBOX* blob = it.data();
TBOX box = blob->bounding_box();
while (!it.at_last() && box.top() <= new_box.top()) {
if (blob == new_blob)
return; // We have it already.
it.forward();
blob = it.data();
box = blob->bounding_box();
}
if (box.top() >= new_box.top()) {
it.add_before_stay_put(new_blob);
needs_refit_ = true;
return;
}
}
needs_refit_ = true;
it.add_after_stay_put(new_blob);
}
// Returns the box gaps between this and its neighbours_ in an array
// indexed by BlobNeighbourDir.
void BLOBNBOX::NeighbourGaps(int gaps[BND_COUNT]) const {
for (int dir = 0; dir < BND_COUNT; ++dir) {
gaps[dir] = MAX_INT16;
BLOBNBOX* neighbour = neighbours_[dir];
if (neighbour != NULL) {
TBOX n_box = neighbour->bounding_box();
if (dir == BND_LEFT || dir == BND_RIGHT) {
gaps[dir] = box.x_gap(n_box);
} else {
gaps[dir] = box.y_gap(n_box);
}
}
}
}
// Moves blobs that look like they don't sit well on a textline from the
// input blobs list to the output small_blobs list.
// This gets them away from initial textline finding to stop diacritics
// from forming incorrect textlines. (Introduced mainly to fix Thai.)
void TextlineProjection::MoveNonTextlineBlobs(
BLOBNBOX_LIST* blobs, BLOBNBOX_LIST* small_blobs) const {
BLOBNBOX_IT it(blobs);
BLOBNBOX_IT small_it(small_blobs);
for (it.mark_cycle_pt(); !it.cycled_list(); it.forward()) {
BLOBNBOX* blob = it.data();
const TBOX& box = blob->bounding_box();
bool debug = AlignedBlob::WithinTestRegion(2, box.left(),
box.bottom());
if (BoxOutOfHTextline(box, NULL, debug) && !blob->UniquelyVertical()) {
blob->ClearNeighbours();
small_it.add_to_end(it.extract());
}
}
}
TBOX box_next_pre_chopped( //get bounding box
BLOBNBOX_IT *it //iterator to blobds
) {
BLOBNBOX *blob; //current blob
TBOX result; //total box
blob = it->data ();
result = blob->bounding_box ();
do {
it->forward ();
blob = it->data ();
}
//until next real blob
while (blob->joined_to_prev ());
return result;
}
// Finds vertical line objects in the given pix.
// Uses the given resolution to determine size thresholds instead of any
// that may be present in the pix.
// The output vertical_x and vertical_y contain a sum of the output vectors,
// thereby giving the mean vertical direction.
// The output vectors are owned by the list and Frozen (cannot refit) by
// having no boxes, as there is no need to refit or merge separator lines.
void LineFinder::FindVerticalLines(int resolution, Pix* pix,
int* vertical_x, int* vertical_y,
TabVector_LIST* vectors) {
#ifdef HAVE_LIBLEPT
Pix* line_pix;
Boxa* boxes = GetVLineBoxes(resolution, pix, &line_pix);
C_BLOB_LIST line_cblobs;
int width = pixGetWidth(pix);
int height = pixGetHeight(pix);
ConvertBoxaToBlobs(width, height, &boxes, &line_cblobs);
// Make the BLOBNBOXes from the C_BLOBs.
BLOBNBOX_LIST line_bblobs;
C_BLOB_IT blob_it(&line_cblobs);
BLOBNBOX_IT bbox_it(&line_bblobs);
for (blob_it.mark_cycle_pt(); !blob_it.cycled_list(); blob_it.forward()) {
C_BLOB* cblob = blob_it.data();
BLOBNBOX* bblob = new BLOBNBOX(cblob);
bbox_it.add_to_end(bblob);
}
ICOORD bleft(0, 0);
ICOORD tright(width, height);
FindLineVectors(bleft, tright, &line_bblobs, vertical_x, vertical_y, vectors);
if (!vectors->empty()) {
// Some lines were found, so erase the unused blobs from the line image
// and then subtract the line image from the source.
bbox_it.move_to_first();
for (bbox_it.mark_cycle_pt(); !bbox_it.cycled_list(); bbox_it.forward()) {
BLOBNBOX* blob = bbox_it.data();
if (blob->left_tab_type() == TT_UNCONFIRMED) {
const TBOX& box = blob->bounding_box();
Box* pixbox = boxCreate(box.left(), height - box.top(),
box.width(), box.height());
pixClearInRect(line_pix, pixbox);
boxDestroy(&pixbox);
}
}
pixDilateBrick(line_pix, line_pix, 1, 3);
pixSubtract(pix, pix, line_pix);
if (textord_tabfind_show_vlines)
pixWrite("vlinesclean.png", line_pix, IFF_PNG);
ICOORD vertical;
vertical.set_with_shrink(*vertical_x, *vertical_y);
TabVector::MergeSimilarTabVectors(vertical, vectors, NULL);
}
pixDestroy(&line_pix);
#endif
}
// Display the blobs in the window colored according to textline quality.
void TextlineProjection::PlotGradedBlobs(BLOBNBOX_LIST* blobs,
ScrollView* win) {
#ifndef GRAPHICS_DISABLED
BLOBNBOX_IT it(blobs);
for (it.mark_cycle_pt(); !it.cycled_list(); it.forward()) {
BLOBNBOX* blob = it.data();
const TBOX& box = blob->bounding_box();
bool bad_box = BoxOutOfHTextline(box, NULL, false);
if (blob->UniquelyVertical())
win->Pen(ScrollView::YELLOW);
else
win->Pen(bad_box ? ScrollView::RED : ScrollView::BLUE);
win->Rectangle(box.left(), box.bottom(), box.right(), box.top());
}
win->Update();
#endif // GRAPHICS_DISABLED
}
// Display the tab codes of the BLOBNBOXes in this grid.
ScrollView* AlignedBlob::DisplayTabs(const char* window_name,
ScrollView* tab_win) {
#ifndef GRAPHICS_DISABLED
if (tab_win == nullptr)
tab_win = MakeWindow(0, 50, window_name);
// For every tab in the grid, display it.
GridSearch<BLOBNBOX, BLOBNBOX_CLIST, BLOBNBOX_C_IT> gsearch(this);
gsearch.StartFullSearch();
BLOBNBOX* bbox;
while ((bbox = gsearch.NextFullSearch()) != nullptr) {
const TBOX& box = bbox->bounding_box();
int left_x = box.left();
int right_x = box.right();
int top_y = box.top();
int bottom_y = box.bottom();
TabType tabtype = bbox->left_tab_type();
if (tabtype != TT_NONE) {
if (tabtype == TT_MAYBE_ALIGNED)
tab_win->Pen(ScrollView::BLUE);
else if (tabtype == TT_MAYBE_RAGGED)
tab_win->Pen(ScrollView::YELLOW);
else if (tabtype == TT_CONFIRMED)
tab_win->Pen(ScrollView::GREEN);
else
tab_win->Pen(ScrollView::GREY);
tab_win->Line(left_x, top_y, left_x, bottom_y);
}
tabtype = bbox->right_tab_type();
if (tabtype != TT_NONE) {
if (tabtype == TT_MAYBE_ALIGNED)
tab_win->Pen(ScrollView::MAGENTA);
else if (tabtype == TT_MAYBE_RAGGED)
tab_win->Pen(ScrollView::ORANGE);
else if (tabtype == TT_CONFIRMED)
tab_win->Pen(ScrollView::RED);
else
tab_win->Pen(ScrollView::GREY);
tab_win->Line(right_x, top_y, right_x, bottom_y);
}
}
tab_win->Update();
#endif
return tab_win;
}
// Returns true if the given blob overlaps more than max_overlaps blobs
// in the current grid.
bool CCNonTextDetect::BlobOverlapsTooMuch(BLOBNBOX* blob, int max_overlaps) {
// Search the grid to see what intersects it.
// Setup a Rectangle search for overlapping this blob.
BlobGridSearch rsearch(this);
TBOX box = blob->bounding_box();
rsearch.StartRectSearch(box);
rsearch.SetUniqueMode(true);
BLOBNBOX* neighbour;
int overlap_count = 0;
while (overlap_count <= max_overlaps &&
(neighbour = rsearch.NextRectSearch()) != NULL) {
if (box.major_overlap(neighbour->bounding_box())) {
++overlap_count;
if (overlap_count > max_overlaps)
return true;
}
}
return false;
}
// Sets up displacement_modes_ with the top few modes of the perpendicular
// distance of each blob from the given direction vector, after rounding.
void BaselineRow::SetupBlobDisplacements(const FCOORD& direction) {
// Set of perpendicular displacements of the blob bottoms from the required
// baseline direction.
GenericVector<double> perp_blob_dists;
displacement_modes_.truncate(0);
// Gather the skew-corrected position of every blob.
double min_dist = MAX_FLOAT32;
double max_dist = -MAX_FLOAT32;
BLOBNBOX_IT blob_it(blobs_);
bool debug = false;
for (blob_it.mark_cycle_pt(); !blob_it.cycled_list(); blob_it.forward()) {
BLOBNBOX* blob = blob_it.data();
const TBOX& box = blob->bounding_box();
#ifdef kDebugYCoord
if (box.bottom() < kDebugYCoord && box.top() > kDebugYCoord) debug = true;
#endif
FCOORD blob_pos((box.left() + box.right()) / 2.0f,
blob->baseline_position());
double offset = direction * blob_pos;
perp_blob_dists.push_back(offset);
if (debug) {
tprintf("Displacement %g for blob at:", offset);
box.print();
}
UpdateRange(offset, &min_dist, &max_dist);
}
// Set up a histogram using disp_quant_factor_ as the bucket size.
STATS dist_stats(IntCastRounded(min_dist / disp_quant_factor_),
IntCastRounded(max_dist / disp_quant_factor_) + 1);
for (int i = 0; i < perp_blob_dists.size(); ++i) {
dist_stats.add(IntCastRounded(perp_blob_dists[i] / disp_quant_factor_), 1);
}
GenericVector<KDPairInc<float, int> > scaled_modes;
dist_stats.top_n_modes(kMaxDisplacementsModes, &scaled_modes);
if (debug) {
for (int i = 0; i < scaled_modes.size(); ++i) {
tprintf("Top mode = %g * %d\n",
scaled_modes[i].key * disp_quant_factor_, scaled_modes[i].data);
}
}
for (int i = 0; i < scaled_modes.size(); ++i)
displacement_modes_.push_back(disp_quant_factor_ * scaled_modes[i].key);
}
// Places a copy of blobs that are near a word (after applying rotation to the
// blob) in the most appropriate word, unless there is doubt, in which case a
// blob can end up in two words. Source blobs are not touched.
void Textord::TransferDiacriticsToWords(BLOBNBOX_LIST* diacritic_blobs,
const FCOORD& rotation,
WordGrid* word_grid) {
WordSearch ws(word_grid);
BLOBNBOX_IT b_it(diacritic_blobs);
// Apply rotation to each blob before finding the nearest words. The rotation
// allows us to only consider above/below placement and not left/right on
// vertical text, because all text is horizontal here.
for (b_it.mark_cycle_pt(); !b_it.cycled_list(); b_it.forward()) {
BLOBNBOX* blobnbox = b_it.data();
TBOX blob_box = blobnbox->bounding_box();
blob_box.rotate(rotation);
ws.StartRectSearch(blob_box);
// Above/below refer to word position relative to diacritic. Since some
// scripts eg Kannada/Telugu habitually put diacritics below words, and
// others eg Thai/Vietnamese/Latin put most diacritics above words, try
// for both if there isn't much in it.
WordWithBox* best_above_word = nullptr;
WordWithBox* best_below_word = nullptr;
int best_above_distance = 0;
int best_below_distance = 0;
for (WordWithBox* word = ws.NextRectSearch(); word != nullptr;
word = ws.NextRectSearch()) {
if (word->word()->flag(W_REP_CHAR)) continue;
TBOX word_box = word->true_bounding_box();
int x_distance = blob_box.x_gap(word_box);
int y_distance = blob_box.y_gap(word_box);
if (x_distance > 0) {
// Arbitrarily divide x-distance by 2 if there is a major y overlap,
// and the word is to the left of the diacritic. If the
// diacritic is a dropped broken character between two words, this will
// help send all the pieces to a single word, instead of splitting them
// over the 2 words.
if (word_box.major_y_overlap(blob_box) &&
blob_box.left() > word_box.right()) {
x_distance /= 2;
}
y_distance += x_distance;
}
if (word_box.y_middle() > blob_box.y_middle() &&
(best_above_word == nullptr || y_distance < best_above_distance)) {
best_above_word = word;
best_above_distance = y_distance;
}
if (word_box.y_middle() <= blob_box.y_middle() &&
(best_below_word == nullptr || y_distance < best_below_distance)) {
best_below_word = word;
best_below_distance = y_distance;
}
}
bool above_good =
best_above_word != nullptr &&
(best_below_word == nullptr ||
best_above_distance < best_below_distance + blob_box.height());
bool below_good =
best_below_word != nullptr && best_below_word != best_above_word &&
(best_above_word == nullptr ||
best_below_distance < best_above_distance + blob_box.height());
if (below_good) {
C_BLOB* copied_blob = C_BLOB::deep_copy(blobnbox->cblob());
copied_blob->rotate(rotation);
// Put the blob into the word's reject blobs list.
C_BLOB_IT blob_it(best_below_word->RejBlobs());
blob_it.add_to_end(copied_blob);
}
if (above_good) {
C_BLOB* copied_blob = C_BLOB::deep_copy(blobnbox->cblob());
copied_blob->rotate(rotation);
// Put the blob into the word's reject blobs list.
C_BLOB_IT blob_it(best_above_word->RejBlobs());
blob_it.add_to_end(copied_blob);
}
}
}
// (Re)Fit a line to the stored points. Returns false if the line
// is degenerate. Althougth the TabVector code mostly doesn't care about the
// direction of lines, XAtY would give silly results for a horizontal line.
// The class is mostly aimed at use for vertical lines representing
// horizontal tab stops.
bool TabVector::Fit(ICOORD vertical, bool force_parallel) {
needs_refit_ = false;
if (boxes_.empty()) {
// Don't refit something with no boxes, as that only happens
// in Evaluate, and we don't want to end up with a zero vector.
if (!force_parallel)
return false;
// If we are forcing parallel, then we just need to set the sort_key_.
ICOORD midpt = startpt_;
midpt += endpt_;
midpt /= 2;
sort_key_ = SortKey(vertical, midpt.x(), midpt.y());
return startpt_.y() != endpt_.y();
}
if (!force_parallel && !IsRagged()) {
// Use a fitted line as the vertical.
DetLineFit linepoints;
BLOBNBOX_C_IT it(&boxes_);
// Fit a line to all the boxes in the list.
for (it.mark_cycle_pt(); !it.cycled_list(); it.forward()) {
BLOBNBOX* bbox = it.data();
TBOX box = bbox->bounding_box();
int x1 = IsRightTab() ? box.right() : box.left();
ICOORD boxpt(x1, box.bottom());
linepoints.Add(boxpt);
if (it.at_last()) {
ICOORD top_pt(x1, box.top());
linepoints.Add(top_pt);
}
}
linepoints.Fit(&startpt_, &endpt_);
if (startpt_.y() != endpt_.y()) {
vertical = endpt_;
vertical -= startpt_;
}
}
int start_y = startpt_.y();
int end_y = endpt_.y();
sort_key_ = IsLeftTab() ? MAX_INT32 : -MAX_INT32;
BLOBNBOX_C_IT it(&boxes_);
// Choose a line parallel to the vertical such that all boxes are on the
// correct side of it.
mean_width_ = 0;
int width_count = 0;
for (it.mark_cycle_pt(); !it.cycled_list(); it.forward()) {
BLOBNBOX* bbox = it.data();
TBOX box = bbox->bounding_box();
mean_width_ += box.width();
++width_count;
int x1 = IsRightTab() ? box.right() : box.left();
// Test both the bottom and the top, as one will be more extreme, depending
// on the direction of skew.
int bottom_y = box.bottom();
int top_y = box.top();
int key = SortKey(vertical, x1, bottom_y);
if (IsLeftTab() == (key < sort_key_)) {
sort_key_ = key;
startpt_ = ICOORD(x1, bottom_y);
}
key = SortKey(vertical, x1, top_y);
if (IsLeftTab() == (key < sort_key_)) {
sort_key_ = key;
startpt_ = ICOORD(x1, top_y);
}
if (it.at_first())
start_y = bottom_y;
if (it.at_last())
end_y = top_y;
}
if (width_count > 0) {
mean_width_ = (mean_width_ + width_count - 1) / width_count;
}
endpt_ = startpt_ + vertical;
needs_evaluation_ = true;
if (start_y != end_y) {
// Set the ends of the vector to fully include the first and last blobs.
startpt_.set_x(XAtY(vertical, sort_key_, start_y));
startpt_.set_y(start_y);
endpt_.set_x(XAtY(vertical, sort_key_, end_y));
endpt_.set_y(end_y);
return true;
}
return false;
}
// Fits a straight baseline to the points. Returns true if it had enough
// points to be reasonably sure of the fitted baseline.
// If use_box_bottoms is false, baselines positions are formed by
// considering the outlines of the blobs.
bool BaselineRow::FitBaseline(bool use_box_bottoms) {
// Deterministic fitting is used wherever possible.
fitter_.Clear();
// Linear least squares is a backup if the DetLineFit produces a bad line.
LLSQ llsq;
BLOBNBOX_IT blob_it(blobs_);
for (blob_it.mark_cycle_pt(); !blob_it.cycled_list(); blob_it.forward()) {
BLOBNBOX* blob = blob_it.data();
if (!use_box_bottoms) blob->EstimateBaselinePosition();
const TBOX& box = blob->bounding_box();
int x_middle = (box.left() + box.right()) / 2;
#ifdef kDebugYCoord
if (box.bottom() < kDebugYCoord && box.top() > kDebugYCoord) {
tprintf("Box bottom = %d, baseline pos=%d for box at:",
box.bottom(), blob->baseline_position());
box.print();
}
#endif
fitter_.Add(ICOORD(x_middle, blob->baseline_position()), box.width() / 2);
llsq.add(x_middle, blob->baseline_position());
}
// Fit the line.
ICOORD pt1, pt2;
baseline_error_ = fitter_.Fit(&pt1, &pt2);
baseline_pt1_ = pt1;
baseline_pt2_ = pt2;
if (baseline_error_ > max_baseline_error_ &&
fitter_.SufficientPointsForIndependentFit()) {
// The fit was bad but there were plenty of points, so try skipping
// the first and last few, and use the new line if it dramatically improves
// the error of fit.
double error = fitter_.Fit(kNumSkipPoints, kNumSkipPoints, &pt1, &pt2);
if (error < baseline_error_ / 2.0) {
baseline_error_ = error;
baseline_pt1_ = pt1;
baseline_pt2_ = pt2;
}
}
int debug = 0;
#ifdef kDebugYCoord
Print();
debug = bounding_box_.bottom() < kDebugYCoord &&
bounding_box_.top() > kDebugYCoord
? 3 : 2;
#endif
// Now we obtained a direction from that fit, see if we can improve the
// fit using the same direction and some other start point.
FCOORD direction(pt2 - pt1);
double target_offset = direction * pt1;
good_baseline_ = false;
FitConstrainedIfBetter(debug, direction, 0.0, target_offset);
// Wild lines can be produced because DetLineFit allows vertical lines, but
// vertical text has been rotated so angles over pi/4 should be disallowed.
// Near vertical lines can still be produced by vertically aligned components
// on very short lines.
double angle = BaselineAngle();
if (fabs(angle) > M_PI * 0.25) {
// Use the llsq fit as a backup.
baseline_pt1_ = llsq.mean_point();
baseline_pt2_ = baseline_pt1_ + FCOORD(1.0f, llsq.m());
// TODO(rays) get rid of this when m and c are no longer used.
double m = llsq.m();
double c = llsq.c(m);
baseline_error_ = llsq.rms(m, c);
good_baseline_ = false;
}
return good_baseline_;
}
// Return true if this vector is the same side, overlaps, and close
// enough to the other to be merged.
bool TabVector::SimilarTo(const ICOORD& vertical,
const TabVector& other, BlobGrid* grid) const {
if ((IsRightTab() && other.IsRightTab()) ||
(IsLeftTab() && other.IsLeftTab())) {
// If they don't overlap, at least in extensions, then there is no chance.
if (ExtendedOverlap(other.extended_ymax_, other.extended_ymin_) < 0)
return false;
// A fast approximation to the scale factor of the sort_key_.
int v_scale = abs(vertical.y());
if (v_scale == 0)
v_scale = 1;
// If they are close enough, then OK.
if (sort_key_ + kSimilarVectorDist * v_scale >= other.sort_key_ &&
sort_key_ - kSimilarVectorDist * v_scale <= other.sort_key_)
return true;
// Ragged tabs get a bigger threshold.
if (!IsRagged() || !other.IsRagged() ||
sort_key_ + kSimilarRaggedDist * v_scale < other.sort_key_ ||
sort_key_ - kSimilarRaggedDist * v_scale > other.sort_key_)
return false;
if (grid == NULL) {
// There is nothing else to test!
return true;
}
// If there is nothing in the rectangle between the vector that is going to
// move, and the place it is moving to, then they can be merged.
// Setup a vertical search for any blob.
const TabVector* mover = (IsRightTab() &&
sort_key_ < other.sort_key_) ? this : &other;
int top_y = mover->endpt_.y();
int bottom_y = mover->startpt_.y();
int left = MIN(mover->XAtY(top_y), mover->XAtY(bottom_y));
int right = MAX(mover->XAtY(top_y), mover->XAtY(bottom_y));
int shift = abs(sort_key_ - other.sort_key_) / v_scale;
if (IsRightTab()) {
right += shift;
} else {
left -= shift;
}
GridSearch<BLOBNBOX, BLOBNBOX_CLIST, BLOBNBOX_C_IT> vsearch(grid);
vsearch.StartVerticalSearch(left, right, top_y);
BLOBNBOX* blob;
while ((blob = vsearch.NextVerticalSearch(true)) != NULL) {
TBOX box = blob->bounding_box();
if (box.top() > bottom_y)
return true; // Nothing found.
if (box.bottom() < top_y)
continue; // Doesn't overlap.
int left_at_box = XAtY(box.bottom());
int right_at_box = left_at_box;
if (IsRightTab())
right_at_box += shift;
else
left_at_box -= shift;
if (MIN(right_at_box, box.right()) > MAX(left_at_box, box.left()))
return false;
}
return true; // Nothing found.
}
return false;
}
// Evaluate the vector in terms of coverage of its length by good-looking
// box edges. A good looking box is one where its nearest neighbour on the
// inside is nearer than half the distance its nearest neighbour on the
// outside of the putative column. Bad boxes are removed from the line.
// A second pass then further filters boxes by requiring that the gutter
// width be a minimum fraction of the mean gutter along the line.
void TabVector::Evaluate(const ICOORD& vertical, TabFind* finder) {
bool debug = false;
needs_evaluation_ = false;
int length = endpt_.y() - startpt_.y();
if (length == 0 || boxes_.empty()) {
percent_score_ = 0;
Print("Zero length in evaluate");
return;
}
// Compute the mean box height.
BLOBNBOX_C_IT it(&boxes_);
int mean_height = 0;
int height_count = 0;
for (it.mark_cycle_pt(); !it.cycled_list(); it.forward()) {
BLOBNBOX* bbox = it.data();
const TBOX& box = bbox->bounding_box();
int height = box.height();
mean_height += height;
++height_count;
}
if (height_count > 0) mean_height /= height_count;
int max_gutter = kGutterMultiple * mean_height;
if (IsRagged()) {
// Ragged edges face a tougher test in that the gap must always be within
// the height of the blob.
max_gutter = kGutterToNeighbourRatio * mean_height;
}
STATS gutters(0, max_gutter + 1);
// Evaluate the boxes for their goodness, calculating the coverage as we go.
// Remove boxes that are not good and shorten the list to the first and
// last good boxes.
int num_deleted_boxes = 0;
bool text_on_image = false;
int good_length = 0;
const TBOX* prev_good_box = NULL;
for (it.mark_cycle_pt(); !it.cycled_list(); it.forward()) {
BLOBNBOX* bbox = it.data();
const TBOX& box = bbox->bounding_box();
int mid_y = (box.top() + box.bottom()) / 2;
if (TabFind::WithinTestRegion(2, XAtY(box.bottom()), box.bottom())) {
if (!debug) {
tprintf("After already deleting %d boxes, ", num_deleted_boxes);
Print("Starting evaluation");
}
debug = true;
}
// A good box is one where the nearest neighbour on the inside is closer
// than half the distance to the nearest neighbour on the outside
// (of the putative column).
bool left = IsLeftTab();
int tab_x = XAtY(mid_y);
int gutter_width;
int neighbour_gap;
finder->GutterWidthAndNeighbourGap(tab_x, mean_height, max_gutter, left,
bbox, &gutter_width, &neighbour_gap);
if (debug) {
tprintf("Box (%d,%d)->(%d,%d) has gutter %d, ndist %d\n",
box.left(), box.bottom(), box.right(), box.top(),
gutter_width, neighbour_gap);
}
// Now we can make the test.
if (neighbour_gap * kGutterToNeighbourRatio <= gutter_width) {
// A good box contributes its height to the good_length.
good_length += box.top() - box.bottom();
gutters.add(gutter_width, 1);
// Two good boxes together contribute the gap between them
// to the good_length as well, as long as the gap is not
// too big.
if (prev_good_box != NULL) {
int vertical_gap = box.bottom() - prev_good_box->top();
double size1 = sqrt(static_cast<double>(prev_good_box->area()));
double size2 = sqrt(static_cast<double>(box.area()));
if (vertical_gap < kMaxFillinMultiple * MIN(size1, size2))
good_length += vertical_gap;
if (debug) {
tprintf("Box and prev good, gap=%d, target %g, goodlength=%d\n",
vertical_gap, kMaxFillinMultiple * MIN(size1, size2),
good_length);
}
} else {
// Adjust the start to the first good box.
SetYStart(box.bottom());
}
prev_good_box = &box;
if (bbox->flow() == BTFT_TEXT_ON_IMAGE)
text_on_image = true;
} else {
// Get rid of boxes that are not good.
if (debug) {
tprintf("Bad Box (%d,%d)->(%d,%d) with gutter %d, ndist %d\n",
box.left(), box.bottom(), box.right(), box.top(),
gutter_width, neighbour_gap);
}
it.extract();
++num_deleted_boxes;
}
}
if (debug) {
Print("Evaluating:");
}
// If there are any good boxes, do it again, except this time get rid of
// boxes that have a gutter that is a small fraction of the mean gutter.
// This filters out ends that run into a coincidental gap in the text.
int search_top = endpt_.y();
int search_bottom = startpt_.y();
int median_gutter = IntCastRounded(gutters.median());
if (gutters.get_total() > 0) {
prev_good_box = NULL;
for (it.mark_cycle_pt(); !it.cycled_list(); it.forward()) {
BLOBNBOX* bbox = it.data();
const TBOX& box = bbox->bounding_box();
int mid_y = (box.top() + box.bottom()) / 2;
// A good box is one where the gutter width is at least some constant
// fraction of the mean gutter width.
bool left = IsLeftTab();
int tab_x = XAtY(mid_y);
int max_gutter = kGutterMultiple * mean_height;
if (IsRagged()) {
// Ragged edges face a tougher test in that the gap must always be
// within the height of the blob.
max_gutter = kGutterToNeighbourRatio * mean_height;
}
int gutter_width;
int neighbour_gap;
finder->GutterWidthAndNeighbourGap(tab_x, mean_height, max_gutter, left,
bbox, &gutter_width, &neighbour_gap);
// Now we can make the test.
if (gutter_width >= median_gutter * kMinGutterFraction) {
if (prev_good_box == NULL) {
// Adjust the start to the first good box.
SetYStart(box.bottom());
search_bottom = box.top();
}
prev_good_box = &box;
search_top = box.bottom();
} else {
// Get rid of boxes that are not good.
if (debug) {
tprintf("Bad Box (%d,%d)->(%d,%d) with gutter %d, mean gutter %d\n",
box.left(), box.bottom(), box.right(), box.top(),
gutter_width, median_gutter);
}
it.extract();
++num_deleted_boxes;
}
}
}
// If there has been a good box, adjust the end.
if (prev_good_box != NULL) {
SetYEnd(prev_good_box->top());
// Compute the percentage of the vector that is occupied by good boxes.
int length = endpt_.y() - startpt_.y();
percent_score_ = 100 * good_length / length;
if (num_deleted_boxes > 0) {
needs_refit_ = true;
FitAndEvaluateIfNeeded(vertical, finder);
if (boxes_.empty())
return;
}
// Test the gutter over the whole vector, instead of just at the boxes.
int required_shift;
if (search_bottom > search_top) {
search_bottom = startpt_.y();
search_top = endpt_.y();
}
double min_gutter_width = kLineCountReciprocal / boxes_.length();
min_gutter_width += IsRagged() ? kMinRaggedGutter : kMinAlignedGutter;
min_gutter_width *= mean_height;
int max_gutter_width = IntCastRounded(min_gutter_width) + 1;
if (median_gutter > max_gutter_width)
max_gutter_width = median_gutter;
int gutter_width = finder->GutterWidth(search_bottom, search_top, *this,
text_on_image, max_gutter_width,
&required_shift);
if (gutter_width < min_gutter_width) {
if (debug) {
tprintf("Rejecting bad tab Vector with %d gutter vs %g min\n",
gutter_width, min_gutter_width);
}
boxes_.shallow_clear();
percent_score_ = 0;
} else if (debug) {
tprintf("Final gutter %d, vs limit of %g, required shift = %d\n",
gutter_width, min_gutter_width, required_shift);
}
} else {
// There are no good boxes left, so score is 0.
percent_score_ = 0;
}
if (debug) {
Print("Evaluation complete:");
}
}
void plot_word_decisions( //draw words
ScrollView *win, //window tro draw in
inT16 pitch, //of block
TO_ROW *row //row to draw
) {
ScrollView::Color colour = ScrollView::MAGENTA; //current colour
ScrollView::Color rect_colour; //fuzzy colour
inT32 prev_x; //end of prev blob
inT16 blob_count; //blobs in word
BLOBNBOX *blob; //current blob
TBOX blob_box; //bounding box
//iterator
BLOBNBOX_IT blob_it = row->blob_list();
BLOBNBOX_IT start_it = blob_it;//word start
rect_colour = ScrollView::BLACK;
prev_x = -MAX_INT16;
blob_count = 0;
for (blob_it.mark_cycle_pt(); !blob_it.cycled_list(); blob_it.forward()) {
blob = blob_it.data();
blob_box = blob->bounding_box();
if (!blob->joined_to_prev()
&& blob_box.left() - prev_x > row->max_nonspace) {
if ((blob_box.left() - prev_x >= row->min_space
|| blob_box.left() - prev_x > row->space_threshold)
&& blob_count > 0) {
if (pitch > 0 && textord_show_fixed_cuts)
plot_fp_cells(win, colour, &start_it, pitch, blob_count,
&row->projection, row->projection_left,
row->projection_right,
row->xheight * textord_projection_scale);
blob_count = 0;
start_it = blob_it;
}
if (colour == ScrollView::MAGENTA)
colour = ScrollView::RED;
else
colour = (ScrollView::Color)(colour + 1);
if (blob_box.left() - prev_x < row->min_space) {
if (blob_box.left() - prev_x > row->space_threshold)
rect_colour = ScrollView::GOLDENROD;
else
rect_colour = ScrollView::CORAL;
//fill_color_index(win, rect_colour);
win->Brush(rect_colour);
win->Rectangle(prev_x, blob_box.bottom(),
blob_box.left(), blob_box.top());
}
}
if (!blob->joined_to_prev())
prev_x = blob_box.right();
if (blob->cblob() != NULL)
blob->cblob()->plot(win, colour, colour);
if (!blob->joined_to_prev() && blob->cblob() != NULL)
blob_count++;
}
if (pitch > 0 && textord_show_fixed_cuts && blob_count > 0)
plot_fp_cells(win, colour, &start_it, pitch, blob_count,
&row->projection, row->projection_left,
row->projection_right,
row->xheight * textord_projection_scale);
}
// Search vertically for a blob that is aligned with the input bbox.
// The search parameters are determined by AlignedBlobParams.
// top_to_bottom tells whether to search down or up.
// The return value is nullptr if nothing was found in the search box
// or if a blob was found in the gutter. On a nullptr return, end_y
// is set to the edge of the search box or the leading edge of the
// gutter blob if one was found.
BLOBNBOX* AlignedBlob::FindAlignedBlob(const AlignedBlobParams& p,
bool top_to_bottom, BLOBNBOX* bbox,
int x_start, int* end_y) {
TBOX box = bbox->bounding_box();
// If there are separator lines, get the column edges.
int left_column_edge = bbox->left_rule();
int right_column_edge = bbox->right_rule();
// start_y is used to guarantee that forward progress is made and the
// search does not go into an infinite loop. New blobs must extend the
// line beyond start_y.
int start_y = top_to_bottom ? box.bottom() : box.top();
if (WithinTestRegion(2, x_start, start_y)) {
tprintf("Column edges for blob at (%d,%d)->(%d,%d) are [%d, %d]\n",
box.left(), box.top(), box.right(), box.bottom(),
left_column_edge, right_column_edge);
}
// Compute skew tolerance.
int skew_tolerance = p.max_v_gap / kMaxSkewFactor;
// Calculate xmin and xmax of the search box so that it contains
// all possibly relevant boxes up to p.max_v_gap above or below accoording
// to top_to_bottom.
// Start with a notion of vertical with the current estimate.
int x2 = (p.max_v_gap * p.vertical.x() + p.vertical.y()/2) / p.vertical.y();
if (top_to_bottom) {
x2 = x_start - x2;
*end_y = start_y - p.max_v_gap;
} else {
x2 = x_start + x2;
*end_y = start_y + p.max_v_gap;
}
// Expand the box by an additional skew tolerance
int xmin = std::min(x_start, x2) - skew_tolerance;
int xmax = std::max(x_start, x2) + skew_tolerance;
// Now add direction-specific tolerances.
if (p.right_tab) {
xmax += p.min_gutter;
xmin -= p.l_align_tolerance;
} else {
xmax += p.r_align_tolerance;
xmin -= p.min_gutter;
}
// Setup a vertical search for an aligned blob.
GridSearch<BLOBNBOX, BLOBNBOX_CLIST, BLOBNBOX_C_IT> vsearch(this);
if (WithinTestRegion(2, x_start, start_y))
tprintf("Starting %s %s search at %d-%d,%d, search_size=%d, gutter=%d\n",
p.ragged ? "Ragged" : "Aligned", p.right_tab ? "Right" : "Left",
xmin, xmax, start_y, p.max_v_gap, p.min_gutter);
vsearch.StartVerticalSearch(xmin, xmax, start_y);
// result stores the best real return value.
BLOBNBOX* result = nullptr;
// The backup_result is not a tab candidate and can be used if no
// real tab candidate result is found.
BLOBNBOX* backup_result = nullptr;
// neighbour is the blob that is currently being investigated.
BLOBNBOX* neighbour = nullptr;
while ((neighbour = vsearch.NextVerticalSearch(top_to_bottom)) != nullptr) {
if (neighbour == bbox)
continue;
TBOX nbox = neighbour->bounding_box();
int n_y = (nbox.top() + nbox.bottom()) / 2;
if ((!top_to_bottom && n_y > start_y + p.max_v_gap) ||
(top_to_bottom && n_y < start_y - p.max_v_gap)) {
if (WithinTestRegion(2, x_start, start_y))
tprintf("Neighbour too far at (%d,%d)->(%d,%d)\n",
nbox.left(), nbox.bottom(), nbox.right(), nbox.top());
break; // Gone far enough.
}
// It is CRITICAL to ensure that forward progress is made, (strictly
// in/decreasing n_y) or the caller could loop infinitely, while
// waiting for a sequence of blobs in a line to end.
// NextVerticalSearch alone does not guarantee this, as there may be
// more than one blob in a grid cell. See comment in AlignTabs.
if ((n_y < start_y) != top_to_bottom || nbox.y_overlap(box))
continue; // Only look in the required direction.
if (result != nullptr && result->bounding_box().y_gap(nbox) > gridsize())
return result; // This result is clear.
if (backup_result != nullptr && p.ragged && result == nullptr &&
backup_result->bounding_box().y_gap(nbox) > gridsize())
return backup_result; // This result is clear.
// If the neighbouring blob is the wrong side of a separator line, then it
// "doesn't exist" as far as we are concerned.
int x_at_n_y = x_start + (n_y - start_y) * p.vertical.x() / p.vertical.y();
if (x_at_n_y < neighbour->left_crossing_rule() ||
x_at_n_y > neighbour->right_crossing_rule())
continue; // Separator line in the way.
int n_left = nbox.left();
int n_right = nbox.right();
int n_x = p.right_tab ? n_right : n_left;
if (WithinTestRegion(2, x_start, start_y))
tprintf("neighbour at (%d,%d)->(%d,%d), n_x=%d, n_y=%d, xatn=%d\n",
nbox.left(), nbox.bottom(), nbox.right(), nbox.top(),
n_x, n_y, x_at_n_y);
if (p.right_tab &&
n_left < x_at_n_y + p.min_gutter &&
n_right > x_at_n_y + p.r_align_tolerance &&
(p.ragged || n_left < x_at_n_y + p.gutter_fraction * nbox.height())) {
// In the gutter so end of line.
if (bbox->right_tab_type() >= TT_MAYBE_ALIGNED)
bbox->set_right_tab_type(TT_DELETED);
*end_y = top_to_bottom ? nbox.top() : nbox.bottom();
if (WithinTestRegion(2, x_start, start_y))
tprintf("gutter\n");
return nullptr;
}
if (!p.right_tab &&
n_left < x_at_n_y - p.l_align_tolerance &&
n_right > x_at_n_y - p.min_gutter &&
(p.ragged || n_right > x_at_n_y - p.gutter_fraction * nbox.height())) {
// In the gutter so end of line.
if (bbox->left_tab_type() >= TT_MAYBE_ALIGNED)
bbox->set_left_tab_type(TT_DELETED);
*end_y = top_to_bottom ? nbox.top() : nbox.bottom();
if (WithinTestRegion(2, x_start, start_y))
tprintf("gutter\n");
return nullptr;
}
if ((p.right_tab && neighbour->leader_on_right()) ||
(!p.right_tab && neighbour->leader_on_left()))
continue; // Neighbours of leaders are not allowed to be used.
if (n_x <= x_at_n_y + p.r_align_tolerance &&
n_x >= x_at_n_y - p.l_align_tolerance) {
// Aligned so keep it. If it is a marked tab save it as result,
// otherwise keep it as backup_result to return in case of later failure.
if (WithinTestRegion(2, x_start, start_y))
tprintf("aligned, seeking%d, l=%d, r=%d\n",
p.right_tab, neighbour->left_tab_type(),
neighbour->right_tab_type());
TabType n_type = p.right_tab ? neighbour->right_tab_type()
: neighbour->left_tab_type();
if (n_type != TT_NONE && (p.ragged || n_type != TT_MAYBE_RAGGED)) {
if (result == nullptr) {
result = neighbour;
} else {
// Keep the closest neighbour by Euclidean distance.
// This prevents it from picking a tab blob in another column.
const TBOX& old_box = result->bounding_box();
int x_diff = p.right_tab ? old_box.right() : old_box.left();
x_diff -= x_at_n_y;
int y_diff = (old_box.top() + old_box.bottom()) / 2 - start_y;
int old_dist = x_diff * x_diff + y_diff * y_diff;
x_diff = n_x - x_at_n_y;
y_diff = n_y - start_y;
int new_dist = x_diff * x_diff + y_diff * y_diff;
if (new_dist < old_dist)
result = neighbour;
}
} else if (backup_result == nullptr) {
if (WithinTestRegion(2, x_start, start_y))
tprintf("Backup\n");
backup_result = neighbour;
} else {
TBOX backup_box = backup_result->bounding_box();
if ((p.right_tab && backup_box.right() < nbox.right()) ||
(!p.right_tab && backup_box.left() > nbox.left())) {
if (WithinTestRegion(2, x_start, start_y))
tprintf("Better backup\n");
backup_result = neighbour;
}
}
}
}
return result != nullptr ? result : backup_result;
}
float Textord::filter_noise_blobs(
BLOBNBOX_LIST *src_list, // original list
BLOBNBOX_LIST *noise_list, // noise list
BLOBNBOX_LIST *small_list, // small blobs
BLOBNBOX_LIST *large_list) { // large blobs
int16_t height; //height of blob
int16_t width; //of blob
BLOBNBOX *blob; //current blob
float initial_x; //first guess
BLOBNBOX_IT src_it = src_list; //iterators
BLOBNBOX_IT noise_it = noise_list;
BLOBNBOX_IT small_it = small_list;
BLOBNBOX_IT large_it = large_list;
STATS size_stats (0, MAX_NEAREST_DIST);
//blob heights
float min_y; //size limits
float max_y;
float max_x;
float max_height; //of good blobs
for (src_it.mark_cycle_pt(); !src_it.cycled_list(); src_it.forward()) {
blob = src_it.data();
if (blob->bounding_box().height() < textord_max_noise_size)
noise_it.add_after_then_move(src_it.extract());
else if (blob->enclosed_area() >= blob->bounding_box().height()
* blob->bounding_box().width() * textord_noise_area_ratio)
small_it.add_after_then_move(src_it.extract());
}
for (src_it.mark_cycle_pt(); !src_it.cycled_list(); src_it.forward()) {
size_stats.add(src_it.data()->bounding_box().height(), 1);
}
initial_x = size_stats.ile(textord_initialx_ile);
max_y = ceil(initial_x *
(tesseract::CCStruct::kDescenderFraction +
tesseract::CCStruct::kXHeightFraction +
2 * tesseract::CCStruct::kAscenderFraction) /
tesseract::CCStruct::kXHeightFraction);
min_y = floor (initial_x / 2);
max_x = ceil (initial_x * textord_width_limit);
small_it.move_to_first ();
for (small_it.mark_cycle_pt (); !small_it.cycled_list ();
small_it.forward ()) {
height = small_it.data()->bounding_box().height();
if (height > max_y)
large_it.add_after_then_move(small_it.extract ());
else if (height >= min_y)
src_it.add_after_then_move(small_it.extract ());
}
size_stats.clear ();
for (src_it.mark_cycle_pt (); !src_it.cycled_list (); src_it.forward ()) {
height = src_it.data ()->bounding_box ().height ();
width = src_it.data ()->bounding_box ().width ();
if (height < min_y)
small_it.add_after_then_move (src_it.extract ());
else if (height > max_y || width > max_x)
large_it.add_after_then_move (src_it.extract ());
else
size_stats.add (height, 1);
}
max_height = size_stats.ile (textord_initialasc_ile);
// tprintf("max_y=%g, min_y=%g, initial_x=%g, max_height=%g,",
// max_y,min_y,initial_x,max_height);
max_height *= tesseract::CCStruct::kXHeightCapRatio;
if (max_height > initial_x)
initial_x = max_height;
// tprintf(" ret=%g\n",initial_x);
return initial_x;
}
int32_t row_words2( //compute space size
TO_BLOCK* block, //block it came from
TO_ROW* row, //row to operate on
int32_t maxwidth, //max expected space size
FCOORD rotation, //for drawing
bool testing_on //for debug
) {
bool prev_valid; //if decent size
bool this_valid; //current blob big enough
int32_t prev_x; //end of prev blob
int32_t min_width; //min interesting width
int32_t valid_count; //good gaps
int32_t total_count; //total gaps
int32_t cluster_count; //no of clusters
int32_t prev_count; //previous cluster_count
int32_t gap_index; //which cluster
int32_t smooth_factor; //for smoothing stats
BLOBNBOX *blob; //current blob
float lower, upper; //clustering parameters
ICOORD testpt;
TBOX blob_box; //bounding box
//iterator
BLOBNBOX_IT blob_it = row->blob_list ();
STATS gap_stats (0, maxwidth);
//gap sizes
float gaps[BLOCK_STATS_CLUSTERS];
STATS cluster_stats[BLOCK_STATS_CLUSTERS + 1];
//clusters
testpt = ICOORD (textord_test_x, textord_test_y);
smooth_factor =
static_cast<int32_t>(block->xheight * textord_wordstats_smooth_factor + 1.5);
// if (testing_on)
// tprintf("Row smooth factor=%d\n",smooth_factor);
prev_valid = false;
prev_x = -INT16_MAX;
const bool testing_row = false;
//min blob size
min_width = static_cast<int32_t>(block->pr_space);
total_count = 0;
for (blob_it.mark_cycle_pt (); !blob_it.cycled_list (); blob_it.forward ()) {
blob = blob_it.data ();
if (!blob->joined_to_prev ()) {
blob_box = blob->bounding_box ();
this_valid = blob_box.width () >= min_width;
if (this_valid && prev_valid
&& blob_box.left () - prev_x < maxwidth) {
gap_stats.add (blob_box.left () - prev_x, 1);
}
total_count++; //count possibles
prev_x = blob_box.right ();
prev_valid = this_valid;
}
}
valid_count = gap_stats.get_total ();
if (valid_count < total_count * textord_words_minlarge) {
gap_stats.clear ();
prev_x = -INT16_MAX;
for (blob_it.mark_cycle_pt (); !blob_it.cycled_list ();
blob_it.forward ()) {
blob = blob_it.data ();
if (!blob->joined_to_prev ()) {
blob_box = blob->bounding_box ();
if (blob_box.left () - prev_x < maxwidth) {
gap_stats.add (blob_box.left () - prev_x, 1);
}
prev_x = blob_box.right ();
}
}
}
if (gap_stats.get_total () == 0) {
row->min_space = 0; //no evidence
row->max_nonspace = 0;
return 0;
}
cluster_count = 0;
lower = block->xheight * words_initial_lower;
upper = block->xheight * words_initial_upper;
gap_stats.smooth (smooth_factor);
do {
prev_count = cluster_count;
cluster_count = gap_stats.cluster (lower, upper,
textord_spacesize_ratioprop,
BLOCK_STATS_CLUSTERS, cluster_stats);
}
while (cluster_count > prev_count && cluster_count < BLOCK_STATS_CLUSTERS);
if (cluster_count < 1) {
row->min_space = 0;
row->max_nonspace = 0;
return 0;
}
for (gap_index = 0; gap_index < cluster_count; gap_index++)
gaps[gap_index] = cluster_stats[gap_index + 1].ile (0.5);
//get medians
if (testing_on) {
tprintf ("cluster_count=%d:", cluster_count);
for (gap_index = 0; gap_index < cluster_count; gap_index++)
tprintf (" %g(%d)", gaps[gap_index],
cluster_stats[gap_index + 1].get_total ());
tprintf ("\n");
}
//Try to find proportional non-space and space for row.
for (gap_index = 0; gap_index < cluster_count
&& gaps[gap_index] > block->max_nonspace; gap_index++);
if (gap_index < cluster_count)
lower = gaps[gap_index]; //most frequent below
else {
if (testing_on)
tprintf ("No cluster below block threshold!, using default=%g\n",
block->pr_nonsp);
lower = block->pr_nonsp;
}
for (gap_index = 0; gap_index < cluster_count
&& gaps[gap_index] <= block->max_nonspace; gap_index++);
if (gap_index < cluster_count)
upper = gaps[gap_index]; //most frequent above
else {
if (testing_on)
tprintf ("No cluster above block threshold!, using default=%g\n",
block->pr_space);
upper = block->pr_space;
}
row->min_space =
static_cast<int32_t>(ceil (upper - (upper - lower) * textord_words_definite_spread));
row->max_nonspace =
static_cast<int32_t>(floor (lower + (upper - lower) * textord_words_definite_spread));
row->space_threshold = (row->max_nonspace + row->min_space) / 2;
row->space_size = upper;
row->kern_size = lower;
if (testing_on) {
if (testing_row) {
tprintf ("GAP STATS\n");
gap_stats.print();
tprintf ("SPACE stats\n");
cluster_stats[2].print_summary();
tprintf ("NONSPACE stats\n");
cluster_stats[1].print_summary();
}
tprintf ("Row at %g has minspace=%d(%g), max_non=%d(%g)\n",
row->intercept (), row->min_space, upper,
row->max_nonspace, lower);
}
return 1;
}
int32_t row_words( //compute space size
TO_BLOCK* block, //block it came from
TO_ROW* row, //row to operate on
int32_t maxwidth, //max expected space size
FCOORD rotation, //for drawing
bool testing_on //for debug
) {
bool testing_row; //contains testpt
bool prev_valid; //if decent size
int32_t prev_x; //end of prev blob
int32_t cluster_count; //no of clusters
int32_t gap_index; //which cluster
int32_t smooth_factor; //for smoothing stats
BLOBNBOX *blob; //current blob
float lower, upper; //clustering parameters
float gaps[3]; //gap clusers
ICOORD testpt;
TBOX blob_box; //bounding box
//iterator
BLOBNBOX_IT blob_it = row->blob_list ();
STATS gap_stats (0, maxwidth);
STATS cluster_stats[4]; //clusters
testpt = ICOORD (textord_test_x, textord_test_y);
smooth_factor =
static_cast<int32_t>(block->xheight * textord_wordstats_smooth_factor + 1.5);
// if (testing_on)
// tprintf("Row smooth factor=%d\n",smooth_factor);
prev_valid = false;
prev_x = -INT32_MAX;
testing_row = false;
for (blob_it.mark_cycle_pt (); !blob_it.cycled_list (); blob_it.forward ()) {
blob = blob_it.data ();
blob_box = blob->bounding_box ();
if (blob_box.contains (testpt))
testing_row = true;
gap_stats.add (blob_box.width (), 1);
}
gap_stats.clear ();
for (blob_it.mark_cycle_pt (); !blob_it.cycled_list (); blob_it.forward ()) {
blob = blob_it.data ();
if (!blob->joined_to_prev ()) {
blob_box = blob->bounding_box ();
if (prev_valid && blob_box.left () - prev_x < maxwidth) {
gap_stats.add (blob_box.left () - prev_x, 1);
}
prev_valid = true;
prev_x = blob_box.right ();
}
}
if (gap_stats.get_total () == 0) {
row->min_space = 0; //no evidence
row->max_nonspace = 0;
return 0;
}
gap_stats.smooth (smooth_factor);
lower = row->xheight * textord_words_initial_lower;
upper = row->xheight * textord_words_initial_upper;
cluster_count = gap_stats.cluster (lower, upper,
textord_spacesize_ratioprop, 3,
cluster_stats);
while (cluster_count < 2 && ceil (lower) < floor (upper)) {
//shrink gap
upper = (upper * 3 + lower) / 4;
lower = (lower * 3 + upper) / 4;
cluster_count = gap_stats.cluster (lower, upper,
textord_spacesize_ratioprop, 3,
cluster_stats);
}
if (cluster_count < 2) {
row->min_space = 0; //no evidence
row->max_nonspace = 0;
return 0;
}
for (gap_index = 0; gap_index < cluster_count; gap_index++)
gaps[gap_index] = cluster_stats[gap_index + 1].ile (0.5);
//get medians
if (cluster_count > 2) {
if (testing_on && textord_show_initial_words) {
tprintf ("Row at %g has 3 sizes of gap:%g,%g,%g\n",
row->intercept (),
cluster_stats[1].ile (0.5),
cluster_stats[2].ile (0.5), cluster_stats[3].ile (0.5));
}
lower = gaps[0];
if (gaps[1] > lower) {
upper = gaps[1]; //prefer most frequent
if (upper < block->xheight * textord_words_min_minspace
&& gaps[2] > gaps[1]) {
upper = gaps[2];
}
}
else if (gaps[2] > lower
&& gaps[2] >= block->xheight * textord_words_min_minspace)
upper = gaps[2];
else if (lower >= block->xheight * textord_words_min_minspace) {
upper = lower; //not nice
lower = gaps[1];
if (testing_on && textord_show_initial_words) {
tprintf ("Had to switch most common from lower to upper!!\n");
gap_stats.print();
}
}
else {
row->min_space = 0; //no evidence
row->max_nonspace = 0;
return 0;
}
}
else {
if (gaps[1] < gaps[0]) {
if (testing_on && textord_show_initial_words) {
tprintf ("Had to switch most common from lower to upper!!\n");
gap_stats.print();
}
lower = gaps[1];
upper = gaps[0];
}
else {
upper = gaps[1];
lower = gaps[0];
}
}
if (upper < block->xheight * textord_words_min_minspace) {
row->min_space = 0; //no evidence
row->max_nonspace = 0;
return 0;
}
if (upper * 3 < block->min_space * 2 + block->max_nonspace
|| lower * 3 > block->min_space * 2 + block->max_nonspace) {
if (testing_on && textord_show_initial_words) {
tprintf ("Disagreement between block and row at %g!!\n",
row->intercept ());
tprintf ("Lower=%g, upper=%g, Stats:\n", lower, upper);
gap_stats.print();
}
}
row->min_space =
static_cast<int32_t>(ceil (upper - (upper - lower) * textord_words_definite_spread));
row->max_nonspace =
static_cast<int32_t>(floor (lower + (upper - lower) * textord_words_definite_spread));
row->space_threshold = (row->max_nonspace + row->min_space) / 2;
row->space_size = upper;
row->kern_size = lower;
if (testing_on && textord_show_initial_words) {
if (testing_row) {
tprintf ("GAP STATS\n");
gap_stats.print();
tprintf ("SPACE stats\n");
cluster_stats[2].print_summary();
tprintf ("NONSPACE stats\n");
cluster_stats[1].print_summary();
}
tprintf ("Row at %g has minspace=%d(%g), max_non=%d(%g)\n",
row->intercept (), row->min_space, upper,
row->max_nonspace, lower);
}
return cluster_stats[2].get_total ();
}
